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Deep Learning for Marine Science

Author: Haiyong Zheng

Publisher: Frontiers Media SA

Published: 2024-05-15

Total Pages: 555

ISBN-13: 2832549055

Deep learning (DL), mainly composed of deep and complex neural networks such as recurrent network and convolutional network, is an emerging research branch in the field of artificial intelligence and machine learning. DL revolution has a far-reaching impact on all scientific disciplines and every corner of our lives. With continuing technological advances, marine science is entering into the big data era with the exponential growth of information. DL is an effective means of harnessing the power of big data. Combined with unprecedented data from cameras, acoustic recorders, satellite remote sensing, and large model outputs, DL enables scientists to solve complex problems in biology, ecosystems, climate, energy, as well as physical and chemical interactions. Although DL has made great strides, it is still only beginning to emerge in many fields of marine science, especially towards representative applications and best practices for the automatic analysis of marine organisms and marine environments. DL in nowadays' marine science mainly leverages cutting-edge techniques of deep neural networks and massive data which collected by in-situ optical or acoustic imaging sensors for underwater applications, such as plankton classification and coral reef detection. This research topic aims to expand the applications of marine science to cover all aspects of detection, classification, segmentation, localization, and density estimation of marine objects, organisms, and phenomena.

Artificial Intelligence Oceanography

Author: Xiaofeng Li

Publisher: Springer Nature

Published: 2023-02-03

Total Pages: 351

ISBN-13: 9811963754

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This open access book invites readers to learn how to develop artificial intelligence (AI)-based algorithms to perform their research in oceanography. Various examples are exhibited to guide details of how to feed the big ocean data into the AI models to analyze and achieve optimized results. The number of scholars engaged in AI oceanography research will increase exponentially in the next decade. Therefore, this book will serve as a benchmark providing insights for scholars and graduate students interested in oceanography, computer science, and remote sensing.

Fish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data

Author: Robert B. Fisher

Publisher: Springer

Published: 2016-03-24

Total Pages: 319

ISBN-13: 3319302086

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This book gives a start-to-finish overview of the whole Fish4Knowledge project, in 18 short chapters, each describing one aspect of the project. The Fish4Knowledge project explored the possibilities of big video data, in this case from undersea video. Recording and analyzing 90 thousand hours of video from ten camera locations, the project gives a 3 year view of fish abundance in several tropical coral reefs off the coast of Taiwan. The research system built a remote recording network, over 100 Tb of storage, supercomputer processing, video target detection and tracking, fish species recognition and analysis, a large SQL database to record the results and an efficient retrieval mechanism. Novel user interface mechanisms were developed to provide easy access for marine ecologists, who wanted to explore the dataset. The book is a useful resource for system builders, as it gives an overview of the many new methods that were created to build the Fish4Knowledge system in a manner that also allows readers to see how all the components fit together.

Deep Learning: Algorithms and Applications

Author: Witold Pedrycz

Publisher: Springer Nature

Published: 2019-10-23

Total Pages: 360

ISBN-13: 3030317609

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This book presents a wealth of deep-learning algorithms and demonstrates their design process. It also highlights the need for a prudent alignment with the essential characteristics of the nature of learning encountered in the practical problems being tackled. Intended for readers interested in acquiring practical knowledge of analysis, design, and deployment of deep learning solutions to real-world problems, it covers a wide range of the paradigm’s algorithms and their applications in diverse areas including imaging, seismic tomography, smart grids, surveillance and security, and health care, among others. Featuring systematic and comprehensive discussions on the development processes, their evaluation, and relevance, the book offers insights into fundamental design strategies for algorithms of deep learning.

Machine Learning Methods in the Environmental Sciences

Author: William W. Hsieh

Publisher: Cambridge University Press

Published: 2009-07-30

Total Pages: 364

ISBN-13: 0521791928

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A graduate textbook that provides a unified treatment of machine learning methods and their applications in the environmental sciences.

Exemplary Practices in Marine Science Education

Author: Géraldine Fauville

Publisher: Springer

Published: 2018-06-28

Total Pages: 452

ISBN-13: 3319907786

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This edited volume is the premier book dedicated exclusively to marine science education and improving ocean literacy, aiming to showcase exemplary practices in marine science education and educational research in this field on a global scale. It informs, inspires, and provides an intellectual forum for practitioners and researchers in this particular context. Subject areas include sections on marine science education in formal, informal and community settings. This book will be useful to marine science education practitioners (e.g. formal and informal educators) and researchers (both education and science).

Spatiotemporal Modeling and Analysis in Marine Science

Author: Junyu He

Publisher: Frontiers Media SA

Published: 2023-11-29

Total Pages: 175

ISBN-13: 2832537448

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With the development of earth observation technologies (such as satellite remote sensing, unmanned aerial vehicle, autonomous underwater vehicle, etc.), an era of big data with important and non-negligible spatial/temporal attributes comes. Novel and rigorous spatiotemporal methodologies and models are needed to process and analyze marine big data. Since many marine environmental processes, such as pollutants diffusion, algae distributions etc., vary or evolve across spatiotemporal domains, detecting the distributions and patterns of marine fauna and, particularly in the coastal regions, will improve our understanding of marine systems and can be beneficial in marine environmental management. The goals of this Research Topic, therefore, are two-fold: (a) to develop methodologies and models in theory and applications, including spatiotemporal geostatistics, geographic information system, deep learning, etc.; (b) to quantitatively gain the knowledge of the marine environment. This Research Topic will provide a platform for researchers to share and exchange their new knowledge gained in a spatiotemporal domain of marine or coastal regions. This Research Topic will cover, but is not limited to, the following areas: • Spatiotemporal variations of physical/chemical/biological indicators (such as chlorophyll, temperature, salinity, colorful dissolved organic matter, suspended solids, nutrients, microplastic, etc.) in marine. • Spatiotemporal variations of potential fishing grounds in marine. • Spatiotemporal variations of the ecosystems in coastal regions, such as salt marshes, mangroves, seagrass, macroalgae, etc. • Spatiotemporal distributions of the pollutants (such as heavy metals, polycyclic aromatic hydrocarbon, etc.) in marine and sediments. • Spatiotemporal evolution pattern modeling and prediction of the marine disasters and abnormal phenomena (such as algal bloom, typhoons, SST anomalies, etc).

Handbook of Deep Learning Applications

Author: Valentina Emilia Balas

Publisher: Springer

Published: 2019-02-25

Total Pages: 383

ISBN-13: 3030114791

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This book presents a broad range of deep-learning applications related to vision, natural language processing, gene expression, arbitrary object recognition, driverless cars, semantic image segmentation, deep visual residual abstraction, brain–computer interfaces, big data processing, hierarchical deep learning networks as game-playing artefacts using regret matching, and building GPU-accelerated deep learning frameworks. Deep learning, an advanced level of machine learning technique that combines class of learning algorithms with the use of many layers of nonlinear units, has gained considerable attention in recent times. Unlike other books on the market, this volume addresses the challenges of deep learning implementation, computation time, and the complexity of reasoning and modeling different type of data. As such, it is a valuable and comprehensive resource for engineers, researchers, graduate students and Ph.D. scholars.

Deep Learning for Unmanned Systems

Author: Anis Koubaa

Publisher: Springer Nature

Published: 2021-10-01

Total Pages: 731

ISBN-13: 3030779394

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This book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture and transmission line inspection to name just a few. These vehicles will benefit from advances of deep learning as a subfield of machine learning able to endow these vehicles with different capability such as perception, situation awareness, planning and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets. In recent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicles that are still open. Moreover, deep learning methods have been recently actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard, deep learning methods such as recent neural network (RNN) and coevolutionary neural networks (CNN). The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering and computer science. The book chapters deal with the recent research problems in the areas of reinforcement learning-based control of UAVs and deep learning for unmanned aerial systems (UAS) The book chapters present various techniques of deep learning for robotic applications. The book chapters contain a good literature survey with a long list of references. The book chapters are well written with a good exposition of the research problem, methodology, block diagrams and mathematical techniques. The book chapters are lucidly illustrated with numerical examples and simulations. The book chapters discuss details of applications and future research areas.

Handbook of Fish Biology and Fisheries

Author: Paul J. B. Hart

Publisher: John Wiley & Sons

Published: 2008-04-15

Total Pages: 432

ISBN-13: 1405123222

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Recent decades have witnessed strong declines in fish stocks aroundthe globe, amid growing concerns about the impact of fisheries onmarine and freshwater biodiversity. Fisheries biologists andmanagers are therefore increasingly asking about aspects ofecology, behaviour, evolution and biodiversity that weretraditionally studied by people working in very separate fields.This has highlighted the need to work more closely together, inorder to help ensure future success both in management andconservation. The Handbook of Fish Biology and Fisheries has beenwritten by an international team of scientists and practitioners,to provide an overview of the biology of freshwater and marine fishspecies together with the science that supports fisheriesmanagement and conservation. This volume, subtitled Fish Biology, reviews a broadvariety of topics from evolutionary relationships and globalbiogeography to physiology, recruitment, life histories, genetics,foraging behaviour, reproductive behaviour and community ecology.The second volume, subtitled Fisheries, uses much of thisinformation in a wide-ranging review of fisheries biology,including methods of capture, marketing, economics, stockassessment, forecasting, ecosystem impacts and conservation. Together, these books present the state of the art in ourunderstanding of fish biology and fisheries and will serve asvaluable references for undergraduates and graduates looking for acomprehensive source on a wide variety of topics in fisheriesscience. They will also be useful to researchers who needup-to-date reviews of topics that impinge on their fields, anddecision makers who need to appreciate the scientific backgroundfor management and conservation of aquatic ecosystems. To order volume I, go to the box in the top right hand corner.Alternatively to order volume II, go to:http://www.blackwellpublishing.com/book.asp?ref=063206482X or toorder the 2 volume set, go to:http://www.blackwellpublishing.com/book.asp?ref=0632064838. Provides a unique overview of the study of fish biology andecology, and the assessment and management of fish populations andecosystems. The first volume concentrates on aspects of fish biology andecology, both at the individual and population levels, whilst thesecond volume addresses the assessment and management of fishpopulations and ecosystems. Written by an international team of expert scientists andpractitioners. An invaluable reference tool for both students, researchers andpractitioners working in the fields of fish biology andfisheries.